The Global Burden of Absenteeism Related to COVID-19 Vaccine Side Effects Among Healthcare Workers: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: A rise in absenteeism among healthcare workers (HCWs) was recorded during the COVID-19 pandemic, mostly attributed to SARS-CoV-2 infections. However, evidence suggests that COVID-19 vaccine-related side effects may have also contributed to absenteeism during this period. This study aimed to synthesize the evidence on the prevalence of absenteeism related to COVID-19 vaccine side effects among HCWs. Methods: The inclusion criteria for this review were original quantitative studies of any design, written in English, that addressed absenteeism related to the side effects of COVID-19 vaccines among HCWs. Four databases (PubMed, Scopus, Embase, and the Web of Science) were searched for eligible articles on 7 June 2024. The risk of bias was assessed using the Newcastle–Ottawa scale. Narrative synthesis and a meta-analysis were used to synthesize the evidence. Results: Nineteen observational studies with 96,786 participants were included. The pooled prevalence of absenteeism related to COVID-19 vaccine side effects was 17% (95% CI: 13–20%), while 83% (95% CI: 80–87%) of the vaccination events did not lead in any absenteeism. Study design, sex, vaccination dose, region, and vaccine type were identified as significant sources of heterogeneity. Conclusions: A non-negligible proportion of HCWs were absent from work after reporting side effects of the COVID-19 vaccine. Various demographic factors should be considered in future vaccination schedules for HCWs to potentially decrease the burden of absenteeism related to vaccine side effects. As most studies included self-reported questionnaire data, our results may be limited due to a recall bias. Other: The protocol of the study was preregistered in the PROSPERO database (CRD42024552517).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.003 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".